Lab course materials for IEMBA 8/9 course "Coding and Artificial Intelligence"

Overview

IEMBA 8/9 - Coding and Artificial Intelligence

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Dear IEMBA 8/9 students,

welcome to our IEMBA 8/9 elective course Coding and Artificial Intelligence, taught by Prof. Dr. Damian Borth and Prof. Dr. Barbara Weber. In this course, lectures and hands-on lab courses alternate to provide a better learning experience. Lab course materials for Python programming, Machine Learning und Deep Learning are available in and accessible through this repository.

Please use a laptop computer for the lab courses (not a tablet) to be able to fully participate in the exercises.

Happy Coding!

Your IEMBA teaching team


This table lists all coding lab notebooks and exercise notebooks:

Date Topic Lab Notebook Exercise Notebook Solution Notebook
< Mon, Jan 17 Prerequisite - Binder
Open In Colab
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Mon, Jan 17 Python 101: Jupyter Notebooks and Python Basics Binder
Open In Colab
Binder
Open In Colab
Binder
Open In Colab
Tue, Jan 18, morning session Python 102: Numerical Math & Images Binder
Open In Colab
Binder
Open In Colab
Binder
Open In Colab
Tue, Jan 18, afternoon session Machine Learning I
(Naive Bayes)
Binder
Open In Colab
Binder
Open In Colab
Binder
Open In Colab
Tue, Jan 18, afternoon session Machine Learning II
(k Nearest-Neighbors)
Binder
Open In Colab
Binder
Open In Colab
Binder
Open In Colab
Wed, Jan 19, morning session Deep Learning I
(Artificial Neural Nets)
Binder
Open In Colab
Binder
Open In Colab
Binder
Open In Colab
Wed, Jan 19, afternoon session Deep Learning II
(Convolutional Neural Nets)
Binder
Open In Colab
Binder
Open In Colab
Binder
Open In Colab
< TBD Exam Exercise - Binder
Open In Colab
-
Owner
Artificial Intelligence & Machine Learning (AI:ML Lab) @ HSG
Deep Learning Research by AIML Team @ HSG
Artificial Intelligence & Machine Learning (AI:ML Lab) @ HSG
[ECCV'20] Convolutional Occupancy Networks

Convolutional Occupancy Networks Paper | Supplementary | Video | Teaser Video | Project Page | Blog Post This repository contains the implementation o

622 Dec 30, 2022
Kaggleship: Kaggle Notebooks

Kaggleship: Kaggle Notebooks This repository contains my Kaggle notebooks. They are generally about data science, machine learning, and deep learning.

Erfan Sobhaei 1 Jan 25, 2022
[ICLR 2021] Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization

Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization Kaidi Cao, Yining Chen, Junwei Lu, Nikos Arechiga, Adrien Gaidon, Tengyu Ma

Kaidi Cao 29 Oct 20, 2022
gtfs2vec - Learning GTFS Embeddings for comparing PublicTransport Offer in Microregions

gtfs2vec This is a companion repository for a gtfs2vec - Learning GTFS Embeddings for comparing PublicTransport Offer in Microregions publication. Vis

Politechnika Wrocławska - repozytorium dla informatyków 5 Oct 10, 2022
An AI made using artificial intelligence (AI) and machine learning algorithms (ML) .

DTech.AIML An AI made using artificial intelligence (AI) and machine learning algorithms (ML) . This is created by help of some members in my team and

1 Jan 06, 2022
A3C LSTM Atari with Pytorch plus A3G design

NEWLY ADDED A3G A NEW GPU/CPU ARCHITECTURE OF A3C FOR SUBSTANTIALLY ACCELERATED TRAINING!! RL A3C Pytorch NEWLY ADDED A3G!! New implementation of A3C

David Griffis 532 Jan 02, 2023
BasicRL: easy and fundamental codes for deep reinforcement learning。It is an improvement on rainbow-is-all-you-need and OpenAI Spinning Up.

BasicRL: easy and fundamental codes for deep reinforcement learning BasicRL is an improvement on rainbow-is-all-you-need and OpenAI Spinning Up. It is

RayYoh 12 Apr 28, 2022
Generate image analogies using neural matching and blending

neural image analogies This is basically an implementation of this "Image Analogies" paper, In our case, we use feature maps from VGG16. The patch mat

Adam Wentz 3.5k Jan 08, 2023
Implementation of the paper "Fine-Tuning Transformers: Vocabulary Transfer"

Transformer-vocabulary-transfer Implementation of the paper "Fine-Tuning Transfo

LEYA 13 Nov 30, 2022
Code for sound field predictions in domains with impedance boundaries. Used for generating results from the paper

Code for sound field predictions in domains with impedance boundaries. Used for generating results from the paper

DTU Acoustic Technology Group 11 Dec 17, 2022
Code for the paper "M2m: Imbalanced Classification via Major-to-minor Translation" (CVPR 2020)

M2m: Imbalanced Classification via Major-to-minor Translation This repository contains code for the paper "M2m: Imbalanced Classification via Major-to

79 Oct 13, 2022
PEPit is a package enabling computer-assisted worst-case analyses of first-order optimization methods.

PEPit: Performance Estimation in Python This open source Python library provides a generic way to use PEP framework in Python. Performance estimation

Baptiste 53 Nov 16, 2022
B2EA: An Evolutionary Algorithm Assisted by Two Bayesian Optimization Modules for Neural Architecture Search

B2EA: An Evolutionary Algorithm Assisted by Two Bayesian Optimization Modules for Neural Architecture Search This is the offical implementation of the

SNU ADSL 0 Feb 07, 2022
Experiments for Operating Systems Lab (ETCS-352)

Operating Systems Lab (ETCS-352) Experiments for Operating Systems Lab (ETCS-352) performed by me in 2021 at uni. All codes are written by me except t

Deekshant Wadhwa 0 Sep 06, 2022
Deep Compression for Dense Point Cloud Maps.

DEPOCO This repository implements the algorithms described in our paper Deep Compression for Dense Point Cloud Maps. How to get started (using Docker)

Photogrammetry & Robotics Bonn 67 Dec 06, 2022
Yas CRNN model training - Yet Another Genshin Impact Scanner

Yas-Train Yet Another Genshin Impact Scanner 又一个原神圣遗物导出器 介绍 该仓库为 Yas 的模型训练程序 相关资料 MobileNetV3 CRNN 使用 假设你会设置基本的pytorch环境。 生成数据集 python main.py gen 训练

wormtql 18 Jan 08, 2023
Pytorch Implementation of Auto-Compressing Subset Pruning for Semantic Image Segmentation

Pytorch Implementation of Auto-Compressing Subset Pruning for Semantic Image Segmentation Introduction ACoSP is an online pruning algorithm that compr

Merantix 8 Dec 07, 2022
Code and real data for the paper "Counterfactual Temporal Point Processes", available at arXiv.

counterfactual-tpp This is a repository containing code and real data for the paper Counterfactual Temporal Point Processes. Pre-requisites This code

Networks Learning 11 Dec 09, 2022
Predicting lncRNA–protein interactions based on graph autoencoders and collaborative training

Predicting lncRNA–protein interactions based on graph autoencoders and collaborative training Code for our paper "Predicting lncRNA–protein interactio

zhanglabNKU 1 Nov 29, 2022